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Found 5 papers, 3 papers with code
On the Reproducibility of Neural Network Predictions
By analyzing the relationship between churn and prediction confidences, we pursue an approach with two components for churn reduction.
Coping with Label Shift via Distributionally Robust Optimisation
The label shift problem refers to the supervised learning setting where the train and test label distributions do not match.
Self-supervised Learning for Large-scale Item Recommendations
Our online results also verify our hypothesis that our framework indeed improves model performance even more on slices that lack supervision.
Robust Large-Margin Learning in Hyperbolic Space
In this paper, we present, to our knowledge, the first theoretical guarantees for learning a classifier in hyperbolic rather than Euclidean space.
Making Deep Neural Networks Robust to Label Noise: a Loss Correction Approach
We present a theoretically grounded approach to train deep neural networks, including recurrent networks, subject to class-dependent label noise.
Ranked #2 on
Image Classification
on Clothing1M (using clean data)
(using extra training data)
